In order to improve drilling efficiency while preserving tool life, the current study focuses on the design and implementation of a simple, optimal fuzzy-control system for drilling force. The main topic of this study is the design and implementation of a networked fuzzy controller. The control system consists of a two-input (force error and change of error), single-output (feed-rate increment) fuzzy controller. The control algorithm is connected to the process through a multipoint interface (MPI) bus. The output (i.e., feed-rate) signal is transmitted through the MPI; therefore, network-induced delay is unavoidable. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) criterion. The main advantage of the approach presented herein is the design of a simple fuzzy controller using a known maximum allowable delay to deal with uncertainties and nonlinearities in the drilling process and delays in the network-based application. The results demonstrate that the proposed control strategy provides an excellent transient response without overshoot and a slightly higher drilling time than the CNC working alone (uncontrolled). Therefore, the fuzzy-control system reduces the influence of the increase in cutting force and torque that occurs as the drill depth increases, thus eliminating the risk of rapid drill wear and catastrophic drill breakage.

The state-of-the-art in nano-turning is evolving rapidly due to the economic and application benefits that nano-scale engineering creates. This paper describes the state of the art in nano-turning with a focus on single point diamond tools (SPDT) and integrated sensory systems. Research in this area has thus far lagged behind other nano-scale research efforts in the chemical and biological sciences. Discussion of the fundamentals of nano-scale turning is presented along with the state-of-the-art in sensor systems. Surface finish is a critical performance characteristic for nano-turned parts, which is detailed in terms of the impact of machining parameters, tool geometry, and material anisotropy. Ongoing research into computer modeling of the cutting mechanics at the nano-scale is presented. Sensors and automation systems for nano-turning are discussed with a focus on active control and in-process adjustments. Finally, recommendations are given for the future of nano-turning and nano-scale machining in general.